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Adequacy Assessment of a Wind-Integrated System Using Neural Network-based Interval Predictions of Wind Power Generation and Load

Abstract : In this paper, we present a modeling and simulation framework for conducting the adequacy assessment of a wind-integrated power system accounting for the associated uncertainties. A multi-perceptron artificial neural network (NN) is trained by a non-dominated sorting genetic algorithm-II (NSGA-II) to forecast point-values and prediction intervals (PIs) of the wind power and load. The output of the assessment is given in terms of point-valued and interval-valued Expected Energy Not Supplied (EENS). We consider different scenarios of wind power and load levels, to explore the influence of the uncertainty in wind and load predictions on the estimation of system adequacy.
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https://hal-supelec.archives-ouvertes.fr/hal-00864843
Contributor : Yanfu Li <>
Submitted on : Monday, September 23, 2013 - 1:27:22 PM
Last modification on : Wednesday, July 15, 2020 - 10:36:10 AM
Long-term archiving on: : Tuesday, December 24, 2013 - 4:31:30 AM

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ak_li_vitelli_zio.pdf
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  • HAL Id : hal-00864843, version 1

Citation

Ronay Ak, Yan-Fu Li, Valeria Vitelli, Enrico Zio. Adequacy Assessment of a Wind-Integrated System Using Neural Network-based Interval Predictions of Wind Power Generation and Load. 2013. ⟨hal-00864843⟩

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